Preparing Distribution Utilities for Utility-scale Storage and Electric Vehicles

2

NREL team

• Adarsh Nagarajan, PhD (Presenter)

• David Palchak

• Shibani Ghosh, PhD

• Aadil Latif, PhD

• Richard Bryce

• Akshay Jain

• Michael Emmanuel, PhD

• Sertac Akar

3

Evolving role of utilities and regulators

4

Beyond classic distribution planning

5

Notable outcomes

Do not oversize, rather optimally

size storage systems

Stage battery deployments

Inclusion of BESS on distribution

grids helps with EV adoption

Rate structures to avoid

coincidental peaks

Place EV charging stations at

distribution primaries

Impact of storage on losses on

network are insignificant

Storage Electric vehicles

Reusable framework for any

distribution utility

6

Notable outcomes

Do not oversize, rather optimally

size storage systems

Stage battery deployments

Inclusion of BESS on distribution

grids helps with EV adoption

Rate structures to avoid

coincidental peaks

Place EV charging stations at

distribution primaries

Impact of storage on losses on

network are insignificant

Storage Electric vehicles

Reusable framework for any

distribution utility

7

Reusable advanced framework for any utility

Baseline Battery Electric Vehicles

• Input Data

• Data Cleaning

• Data Conversion

• Detailed Grid Models

• Storage Sizing

• Storage Siting

• Storage Control

• EV Modeling

• EV Locations

• Charger types

• EV profiles

MO

DE

LIN

G

Simulation

Architecture

SIM

UL

AT

ION

Technical

Analysis

Economic

Analysis

• Grid Readiness

• DER Settings

• Comparisons

X

Y

Z

AN

ALY

SIS

8

Notable outcomes

Do not oversize, rather optimally

size storage systems

Stage battery deployments

Inclusion of BESS on distribution

grids helps with EV adoption

Rate structures to avoid

coincidental peaks

Place EV charging stations at

distribution primaries

Impact of storage on losses are

insignificant

Storage Electric vehicles

Reusable framework for any

distribution utility

9

Do not oversize, rather optimally size storage

systems

Battery is sized to meet the 70th percentile of overloading instances

during a year.

Overloading instance: The peak power and energy from the time point at

which the loading exceeds some threshold until the loading again falls

below that threshold.

kWh𝑖 = PF ∗ නt1

t2

(kVA(t) − 𝑻)dt

kW𝑖 = PF ∗ 𝑚𝑎𝑥 kVA t − 𝑻 ∀ t ∈ (t1, t2)

DT 29504798: rated at 990 kVA

T = 693 kVA

70th percentile

10

Load duration curve-based storage control

Not every transformer needs the same charging

and discharging thresholds

11

Charging and discharging thresholds on battery

life• The number of transitions also follows the trend predicted based on load duration curves.

Differences in strategies: 29504793 > 29506095 > 29504798 > 29511321

Nu

mb

er

of tr

ansitio

ns

1 2 3 4 5 6 7 8 9 10

Nu

mb

er

of tr

ansitio

ns

1 2 3 4 5 6 7 8 9 10

Nu

mb

er

of tr

ansitio

ns

1 2 3 4 5 6 7 8 9 10

Simulated Years

Nu

mb

er

of tr

ansitio

ns

1 2 3 4 5 6 7 8 9 10

Simulated Years

12

Notable outcomes

Do not oversize, rather optimally

size storage systems

Stage battery deployments

Unmet and unidentified needs

to monetize storage services

Inclusion of BESS on distribution

grids helps with EV adoption

Rate structures to avoid

coincidental peaks

Place EV charging stations at

distribution primaries

Impact of storage on losses are

insignificant

Storage Electric vehicles

13

DT Losses Comparison

Due to operation in higher efficiency region DTs with storage generally

experience reduction in losses compared with the baseline values.

14

Notable outcomes

Do not oversize, rather optimally

size storage systems

Stage battery deployments

Unmet and unidentified needs

to monetize storage services

Inclusion of BESS on distribution

grids helps with EV adoption

Rate structures to avoid

coincidental peaks

Place EV charging stations at

distribution primaries

Impact of storage on losses are

insignificant

Storage Electric vehicles

15

Long Term Distribution Planning Options

• Baseline violations can be corrected by replacing existing equipment with

higher rating equipment or by operating multiple devices in parallel.

• The other option is to use emerging technologies such as battery storage

systems to reduce equipment loading.

• Both options were compared in the simulations.

Traditional Approach

Emerging Technologies

16

Standard vs. Staged Deployment Scenarios

The staged deployment is designed for meeting the capacity requirements from the feeder line / transformer

upgradesTotal Battery Capacity (kWh) 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028

Standard Deployment (Scenario-1) 3,600 3,600 3,600 3,600 3,600 3,600 3,600 3,600 3,600 3,600

Staged Deployment (Scenario-2) 2,000 2,400 2,400 3,000 3,000 3,000 3,600 3,600 3,600 3,600

Staged Deployment (Scenario-3) 1,800 2,000 2,200 2,400 2,600 2,800 3,000 3,200 3,600 3,600

Battery Capacity Required (kWh) 1,474 1,760 2,055 2,329 2,556 2,840 3,054 3,287 3,560 3,560

17

Standard vs Staged Deployment Scenarios

18

Notable outcomes

Do not oversize, rather optimally

size storage systems

Stage battery deployments

Inclusion of BESS on distribution

grids helps with EV adoption

Rate structures to avoid

coincidental peaks

Place EV charging stations at

distribution primaries

Impact of storage on losses are

insignificant

Storage Electric vehicles

Reusable framework for any

distribution utility

19

DT Load Duration curves with EV and BESS

• EVs and BESS fill DT valleys and push operation in higher efficiency region.

• 28% more energy flows through this DT due to EVs

Increased efficiency with

managed EV charging

Increased efficiency with

managed EV and BESS

charging

20

Upgrade deferrals with BESS for secondary EVs

• With EVs 126 line

segments experienced

greater than 100%

overloading.

• Traditional thermal

upgrades of baseline

violations could not

mitigate EV thermal

violations.

• BESS reduced line

violations to just 19 line

segments, an 85%

reduction in violations.

21

Key Takeaways

• Battery energy storage if properly used is highly beneficial

• Battery storage provides additional performance criteria that additional

transformer upgrades alone do not.

• Storage brings device upgrade deferrals and yet enables EV penetration

• Reusable and scalable framework developed for utilities to accommodate

emerging technologies

• Staged deployment scenario results in additional cost savings

– 13.2% savings was shown with a specified deployment scenario

– This scenario allows for flexibility to the upgrade

This work was authored, in part, by the National Renewable Energy Laboratory

(NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department

of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by

the United States Agency for International Development (USAID) under Contract

No. IAG-17-2050. The views expressed in this report do not necessarily represent

the views of the DOE or the U.S. Government, or any agency thereof, including

USAID. The U.S. Government retains and the publisher, by accepting the article for

publication, acknowledges that the U.S. Government retains a nonexclusive, paid-

up, irrevocable, worldwide license to publish or reproduce the published form of this

work, or allow others to do so, for U.S. Government purposes.

Thank you!

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